Cache-Conscious Index Mechanism for Main-Memory Databases

Recent studies have addressed that the cache be havior is important in the design of main memory index structures. Cache-conscious indices such as the CSB^＋-tree are shown to outperform conventional main memory indices such as the AVL-tree and the T-tree. This paper proposes a cacheconscious version of the T-tree, CST-tree, defined according to the cache-conscious definition. To separate the keys within a node into two parts, the CST-tree can gain higher cache hit ratio.

Enhancing AI System Privacy:An Automatic Tool for Achieving GDPR Compliance in NoSQL Databases

The EU’s Artificial Intelligence Act(AI Act)imposes requirements for the privacy compliance of AI systems.AI systems must comply with privacy laws such as the GDPR when providing services.These laws provide users with the right to issue a Data Subject Access Request(DSAR).Responding to such requests requires database administrators to identify information related to an individual accurately.However,manual compliance poses significant challenges and is error-prone.Database administrators need to write queries through time-consuming labor.The demand for large amounts of data by AI systems has driven the development of NoSQL databases.Due to the flexible schema of NoSQL databases,identifying personal information becomes even more challenging.This paper develops an automated tool to identify personal information that can help organizations respond to DSAR.Our tool employs a combination of various technologies,including schema extraction of NoSQL databases and relationship identification from query logs.We describe the algorithm used by our tool,detailing how it discovers and extracts implicit relationships from NoSQL databases and generates relationship graphs to help developers accurately identify personal data.We evaluate our tool on three datasets,covering different database designs,achieving an F1 score of 0.77 to 1.Experimental results demonstrate that our tool successfully identifies information relevant to the data subject.Our tool reduces manual effort and simplifies GDPR compliance,showing practical application value in enhancing the privacy performance of NOSQL databases and AI systems.

Databases of 2D material-substrate interfaces and 2D charged building blocks

Discovery of materials using“bottom-up”or“top-down”approach is of great interest in materials science.Layered materials consisting of two-dimensional(2D)building blocks provide a good platform to explore new materials in this respect.In van der Waals(vdW)layered materials,these building blocks are charge neutral and can be isolated from their bulk phase(top-down),but usually grow on substrate.In ionic layered materials,they are charged and usually cannot exist independently but can serve as motifs to construct new materials(bottom-up).In this paper,we introduce our recently constructed databases for 2D material-substrate interface(2DMSI),and 2D charged building blocks.For 2DMSI database,we systematically build a workflow to predict appropriate substrates and their geometries at substrates,and construct the 2DMSI database.For the 2D charged building block database,1208 entries from bulk material database are identified.Information of crystal structure,valence state,source,dimension and so on is provided for each entry with a json format.We also show its application in designing and searching for new functional layered materials.The 2DMSI database,building block database,and designed layered materials are available in Science Data Bank at https://doi.org/10.57760/sciencedb.j00113.00188.

Recent Advances in In-Memory Computing:Exploring Memristor and Memtransistor Arrays with 2D Materials

The conventional computing architecture faces substantial chal-lenges,including high latency and energy consumption between memory and processing units.In response,in-memory computing has emerged as a promising alternative architecture,enabling computing operations within memory arrays to overcome these limitations.Memristive devices have gained significant attention as key components for in-memory computing due to their high-density arrays,rapid response times,and ability to emulate biological synapses.Among these devices,two-dimensional(2D)material-based memristor and memtransistor arrays have emerged as particularly promising candidates for next-generation in-memory computing,thanks to their exceptional performance driven by the unique properties of 2D materials,such as layered structures,mechanical flexibility,and the capability to form heterojunctions.This review delves into the state-of-the-art research on 2D material-based memristive arrays,encompassing critical aspects such as material selection,device perfor-mance metrics,array structures,and potential applications.Furthermore,it provides a comprehensive overview of the current challenges and limitations associated with these arrays,along with potential solutions.The primary objective of this review is to serve as a significant milestone in realizing next-generation in-memory computing utilizing 2D materials and bridge the gap from single-device characterization to array-level and system-level implementations of neuromorphic computing,leveraging the potential of 2D material-based memristive devices.

A dynamic database of solid-state electrolyte(DDSE)picturing all-solid-state batteries

All-solid-state batteries(ASSBs)are a class of safer and higher-energy-density materials compared to conventional devices,from which solid-state electrolytes(SSEs)are their essential components.To date,investigations to search for high ion-conducting solid-state electrolytes have attracted broad concern.However,obtaining SSEs with high ionic conductivity is challenging due to the complex structural information and the less-explored structure-performance relationship.To provide a solution to these challenges,developing a database containing typical SSEs from available experimental reports would be a new avenue to understand the structureperformance relationships and find out new design guidelines for reasonable SSEs.Herein,a dynamic experimental database containing>600 materials was developed in a wide range of temperatures(132.40–1261.60 K),including mono-and divalent cations(e.g.,Li^(+),Na^(+),K^(+),Ag^(+),Ca^(2+),Mg^(2+),and Zn^(2+))and various types of anions(e.g.,halide,hydride,sulfide,and oxide).Data-mining was conducted to explore the relationships among different variates(e.g.,transport ion,composition,activation energy,and conductivity).Overall,we expect that this database can provide essential guidelines for the design and development of high-performance SSEs in ASSB applications.This database is dynamically updated,which can be accessed via our open-source online system.

Type-2 Neutrosophic Set and Their Applications in Medical Databases Deadlock Resolution

Electronic patient data gives many advantages,but also new difficulties.Deadlocks may delay procedures like acquiring patient information.Distributed deadlock resolution solutions introduce uncertainty due to inaccurate transaction properties.Soft computing-based solutions have been developed to solve this challenge.In a single framework,ambiguous,vague,incomplete,and inconsistent transaction attribute information has received minimal attention.The work presented in this paper employed type-2 neutrosophic logic,an extension of type-1 neutrosophic logic,to handle uncertainty in real-time deadlock-resolving systems.The proposed method is structured to reflect multiple types of knowledge and relations among transactions’features that include validation factor degree,slackness degree,degree of deadline-missed transaction based on the degree of membership of truthiness,degree ofmembership of indeterminacy,and degree ofmembership of falsity.Here,the footprint of uncertainty(FOU)for truth,indeterminacy,and falsity represents the level of uncertainty that exists in the value of a grade of membership.We employed a distributed real-time transaction processing simulator(DRTTPS)to conduct the simulations and conducted experiments using the benchmark Pima Indians diabetes dataset(PIDD).As the results showed,there is an increase in detection rate and a large drop in rollback rate when this new strategy is used.The performance of Type-2 neutrosophicbased resolution is better than the Type-1 neutrosophic-based approach on the execution ratio scale.The improvement rate has reached 10%to 20%,depending on the number of arrived transactions.

Analysis of E2F3 gene variants,expression and clinical significance in melanoma based on multiple databases

Objective:To investigate the variation,expression and clinical significance of E2F3 gene in melanoma.Methods:Firstly,cBioportal database,Oncomine database and GEO database were used to analyze the variation and expression level of E2F3 gene in melanoma.OSskcm database and TISIDB database were used to analyze the relationship between E2F3 and melanoma prognosis and tumor immune infiltrating cells.Then,the LinkedOmics database was used to identify the differential genes related to E2F3 expression in melanoma and analyze their biological functions.Finally,small molecule compounds for the treatment of melanoma were screened through the CMap database.Results:The mutation rate of E2F3 gene in melanoma is about 4%,and there are 21 mutation sites.Compared with normal skin tissues,the expression of E2F3 gene in melanoma was significantly increased(P<0.01).The mutation and increased expression of E2F3 gene were associated with the shortened overall survival(OS)of melanoma patients(P<0.05).The CNA level of E2F3 was negatively correlated with the expression levels of lymphocytes such as pDC,Neutrophil,Act DC and Th17,and negatively correlated with the expression levels of chemokines such as CXCL5,CCL13 and CCR1.The methylation level of E2F3 was positively correlated with the expression levels of Th1,Neutrophil,Act DC and other lymphocytes,and positively correlated with the expression levels of CXCL16,CXCL12,CCR1 and other chemokines.The expression level of E2F3 was negatively correlated with the expression levels of lymphocytes such as Th17,Tcm CD4 and Th1,and negatively correlated with the expression levels of chemokines such as CXCL 16,CCL 22 and CCL 2.The expression of 96 genes such as UHRF1BP1 in melanoma was significantly correlated with the expression of E2F3(|cor|0.5,P<0.05).The above genes were mainly related to RNA transport,eukaryotic ribosome biogenesis,cell cycle and other pathways.Among them,WDR12,WDR43,RBM28,UTP18,DKC1,PAK1IP1,DDX31,TEX10,TRUB1 and TRMT61B were the top 10 hub genes.YC-1,simvastatin,cytochalasin-d,Deforolimus and cytochalasin-b may be five small molecule compounds for the treatment of melanoma.Conclusion:The mutation and increased expression level of E2F3 gene are related to the poor prognosis of melanoma and participate in the occurrence and development of melanoma by affecting the expression of different tumor immune infiltrating cell subtypes,which may be a potential diagnostic marker and therapeutic target for melanoma.

Discrimination of polysorbate 20 by high-performance liquid chromatography-charged aerosol detection and characterization for components by expanding compound database and library

Analyzing polysorbate 20(PS20)composition and the impact of each component on stability and safety is crucial due to formulation variations and individual tolerance.The similar structures and polarities of PS20 components make accurate separation,identification,and quantification challenging.In this work,a high-resolution quantitative method was developed using single-dimensional high-performance liquid chromatography(HPLC)with charged aerosol detection(CAD)to separate 18 key components with multiple esters.The separated components were characterized by ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UHPLC-Q-TOF-MS)with an identical gradient as the HPLC-CAD analysis.The polysorbate compound database and library were expanded over 7-time compared to the commercial database.The method investigated differences in PS20 samples from various origins and grades for different dosage forms to evaluate the composition-process relationship.UHPLC-Q-TOF-MS identified 1329 to 1511 compounds in 4 batches of PS20 from different sources.The method observed the impact of 4 degradation conditions on peak components,identifying stable components and their tendencies to change.HPLC-CAD and UHPLC-Q-TOF-MS results provided insights into fingerprint differences,distinguishing quasi products.

Comprehensive analysis of advanced glycation end-products in commonly consumed foods:presenting a database for dietary AGEs and associated exposure assessment

Advanced glycation end-products(AGEs)are a group of heterogeneous compounds formed in heatprocessed foods and are proven to be detrimental to human health.Currently,there is no comprehensive database for AGEs in foods that covers the entire range of food categories,which limits the accurate risk assessment of dietary AGEs in human diseases.In this study,we first established an isotope dilution UHPLCQq Q-MS/MS-based method for simultaneous quantification of 10 major AGEs in foods.The contents of these AGEs were detected in 334 foods covering all main groups consumed in Western and Chinese populations.Nε-Carboxymethyllysine,methylglyoxal-derived hydroimidazolone isomers,and glyoxal-derived hydroimidazolone-1 are predominant AGEs found in most foodstuffs.Total amounts of AGEs were high in processed nuts,bakery products,and certain types of cereals and meats(>150 mg/kg),while low in dairy products,vegetables,fruits,and beverages(<40 mg/kg).Assessment of estimated daily intake implied that the contribution of food groups to daily AGE intake varied a lot under different eating patterns,and selection of high-AGE foods leads to up to a 2.7-fold higher intake of AGEs through daily meals.The presented AGE database allows accurate assessment of dietary exposure to these glycotoxins to explore their physiological impacts on human health.

Database Search Behaviors: Insight from a Survey of Information Retrieval Practices

This study examines the database search behaviors of individuals, focusing on gender differences and the impact of planning habits on information retrieval. Data were collected from a survey of 198 respondents, categorized by their discipline, schooling background, internet usage, and information retrieval preferences. Key findings indicate that females are more likely to plan their searches in advance and prefer structured methods of information retrieval, such as using library portals and leading university websites. Males, however, tend to use web search engines and self-archiving methods more frequently. This analysis provides valuable insights for educational institutions and libraries to optimize their resources and services based on user behavior patterns.

Application of Thermodynamic Database to Corrosion of ZrO_(2) Containing Submerged Entry Nozzle in Steel Continuous Casting Process

The CALPHAD thermodynamic databases are very useful to analyze the complex chemical reactions happening in high temperature material process.The FactSage thermodynamic database can be used to calculate complex phase diagrams and equilibrium phases involving refractories in industrial process.In this study,the FactSage thermodynamic database relevant to ZrO_(2)-based refractories was reviewed and the application of the database to understanding the corrosion of continuous casting nozzle refractories in steelmaking was presented.

Impact of index admission cholecystectomy vs interval cholecystectomy on readmission rate in acute cholangitis: National Readmission Database survey

BACKGROUND Elective cholecystectomy(CCY)is recommended for patients with gallstone-related acute cholangitis(AC)following endoscopic decompression to prevent recurrent biliary events.However,the optimal timing and implications of CCY remain unclear.AIM To examine the impact of same-admission CCY compared to interval CCY on patients with gallstone-related AC using the National Readmission Database(NRD).METHODS We queried the NRD to identify all gallstone-related AC hospitalizations in adult patients with and without the same admission CCY between 2016 and 2020.Our primary outcome was all-cause 30-d readmission rates,and secondary outcomes included in-hospital mortality,length of stay(LOS),and hospitalization cost.RESULTS Among the 124964 gallstone-related AC hospitalizations,only 14.67%underwent the same admission CCY.The all-cause 30-d readmissions in the same admission CCY group were almost half that of the non-CCY group(5.56%vs 11.50%).Patients in the same admission CCY group had a longer mean LOS and higher hospitalization costs attrib-utable to surgery.Although the most common reason for readmission was sepsis in both groups,the second most common reason was AC in the interval CCY group.CONCLUSION Our study suggests that patients with gallstone-related AC who do not undergo the same admission CCY have twice the risk of readmission compared to those who undergo CCY during the same admission.These readmis-sions can potentially be prevented by performing same-admission CCY in appropriate patients,which may reduce subsequent hospitalization costs secondary to readmissions.

WEIGHTED ALGORITHM FOR FINGERPRINTING RELATIONAL DATABASES

The necessity and the feasibility of introducing attribute weight into digital fingerprinting system are given. The weighted algorithm for fingerprinting relational databases of traitor tracing is proposed. Higher weights are assigned to more significant attributes, so important attributes are more frequently fingerprinted than other ones. Finally, the robustness of the proposed algorithm, such as performance against collusion attacks, is analyzed. Experimental results prove the superiority of the algorithm.

Scaling up the DBSCAN Algorithm for Clustering Large Spatial Databases Based on Sampling Technique

Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases.

DATA MODEL AND VISUALIZATION OF 3D CITY LANDSCAPE BASED ON INTEGRATED DATABASES

Data acquisition and modeling are the two important, difficult and costful aspects in a Cybercity project. 2D-GIS is mature and can manage a lot of spatial data. Thus 3D-GIS should make the best of data and technology of 2D-GIS. Construction of a useful synthetic environment requires integration of multiple types of information like DEM, texture images and 3D representation of objects such as buildings. In this paper, the method for 3D city landscape data model and visualization based on integrated databases is presented. Since the data volume of raster are very huge, special strategies(for example, pyramid gridded method) must be adopted in order to manage raster data efficiently. Three different methods of data acquisition, the proper data structure and a simple modeling method are presented as well. At last, a pilot project of Shanghai Cybercity is illustrated.

Rules-based object-relational databases ontology construction

To solve the problems of shaving and reusing information in the information system, a rules-based ontology constructing approach from object-relational databases is proposed. A 3-tuple ontology constructing model is proposed first. Then, four types of ontology constructing rules including class, property, property characteristics, and property restrictions ave formalized according to the model. Experiment results described in Web ontology language prove that our proposed approach is feasible for applying in the semantic objects project of semantic computing laboratory in UC Irvine. Our approach reduces about twenty percent constructing time compared with the ontology construction from relational databases.

Time series analysis of mantle cycles Part Ⅰ:Periodicities and correlations among seven global isotopic databases

In this study,seven isotopic databases are presented and analyzed to identify mantle and crustal episodes on a global scale by focusing on periodicity ranging from 70 to 200 million years(Myr).The databases are the largest,or among the largest,compiled for each type of data-with an objective of finding some samples from every region of every continent,to make each database as global as conceivably possible.The databases contain zircon Lu/Hf isotopic data,whole-rock Sm/Nd isotopic data,U/Pb detrital zircon ages,U/Pb igneous zircon ages,U/Pb non-zircon ages,whole-rock Re/Os isotopic data,and large igneous province ages.Part I of this study focuses on the periodicities of age histograms and geochemical averages developed from the seven databases,via spectral and cross-correlation analyses.Natural physical cycles often propagate in exact integer multiples of a fundamental cycle,referred to as harmonics.The tests show that harmonic geological cycles of^93.5 and^187 Myr have persisted throughout terrestrial history,and the cyclicities are statistically significant for U/Pb igneous zircon ages,U/Pb detrital zircon ages,U/Pb zircon-rim ages,large igneous province ages,meanεHf(t)for all samples,meanεHf(t)values for igneous-only samples,and relative abundance of mafic rocks.Equally important,cross-correlation analyses show these seven time-series are nearly synchronous(±7 Myr)with a model consisting of periodicities of 93.5 and 187 Myr.Additionally,the similarities between peaks in the 93.5 and 187 Myr mantle cycles and terminal ages of established and suspected superchrons provide a framework for predicting and testing superchron periodicity.

THE OPTIMAL TRUNCATED LOW-DIMENSIONAL DYNAMICAL SYSTEMS BASED ON FLOW DATABASES

A new theory on the construction of optimal truncated Low-Dimensional Dynamical Systems (LDDSs) with different physical meanings has been developed, The physical properties of the optimal bases are reflected in the user-defined optimal conditions, Through the analysis of linear and nonlinear examples, it is shown that the LDDSs constructed by using the Proper Orthogonal Decomposition (POD) method are not the optimum. After comparing the errors of LDDSs based on the new theory POD and Fourier methods, it is concluded that the LDDSs based on the new theory are optimally truncated and catch the desired physical properties of the systems.

Accessing Web Heterogeneous Databases Based on CORBA

The future usage of heterogeneous databases will consist of the WWW and CORBA environments. The integration of the WWW databases and CORBA standards are discussed. These two techniques need to merge together to make distributed usage of heterogeneous databases user friendly. In an environment integrating WWW databases and CORBA technologies, CORBA can be used to access heterogeneous data sources in the internet. This kind of applications can achieve distributed transactions to assure data consistency and integrity. The application of this technology is with a good prospect.

DCAD:a Dual Clustering Algorithm for Distributed Spatial Databases

Spatial objects have two types of attributes： geometrical attributes and non-geometrical attributes, which belong to two different attribute domains （geometrical and non-geometrical domains）. Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering： local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted clustering is obtained based on those features fective and efficient. Second, local features from each site are sent to a central site where global Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.